PID control schemes have been widely used in most of process control systems. Most of these processes are often treated as first-order systems with dead times. And also, in many cases, PID parameters are usually tuned based on the process parameters, i. e. , the time constant, the dead time and the process gain. However, since these process parameters can not be obtained exactly, it is well known that it is difficult to find the suitable PID parameters in practice. In this paper, we propose a genetic tuning scheme of PID parameters for first-order systems with large dead times. The authors have already proposed a tuning method of PID parameters using a genetic algorithm (GA), which was based on the relationship between PID control and generalized minimum variance control(GMVC) laws. In practice, for large dead time systems, first-order low pass pre-filters are often used. The proposed method is an extended version of the previously proposed method mentioned above to the system with a pre-filter due to the large dead time, i. e. , a tuning method of both PID parameters and the pre-filter using a GA. The proposed control scheme is numerically evaluated on some simulation examples.

We have developed a new efficient neural network-based algorithm for Alife application in a competitive world whereby the effects of interactions among organisms are evaluated in a weak form by exploiting the position of nearest food elements into consideration but not the positions of the other competing organisms. Two online learning algorithms, an instructive ASL (adaptive supervised learning) and an evaluative feedback-oriented RL (reinforcement learning) algorithm developed have been tested in simulating Alife environments with various neural network algorithms. The constructive compound neural network algorithm FuzGa guided by the ASL learning algorithm has proved to be most efficient among the methods experimented including the classical constructive cascaded CasCor algorithm of [18],[19] and the fixed non-constructive fuzzy neural networks. Adopting an adaptively selected best sequence of feedback action period Δα which we have found to be a decisive parameter in improving the network efficiency, the ASL-guided FuzGa had a performance of an averaged fitness value of 541.8 (standard deviation 48.8) as compared with 500(53.8) for ASL-guided CasCor and 489.2 (39.7) for RL-guided FuzGa. Our FuzGa algorithm has also outperformed the CasCor in time complexity by 31.1%. We have elucidated how the dimensionless parameter food availability FA representing the intensity of interactions among the organisms relates to a best sequence of the feedback action period Δα and an optimal number of hidden neurons for the given configuration of the networks. We confirm that the present solution successfully evaluates the effect of interactions at a larger FA, reducing to an isolated solution at a lower value of FA. The simulation is carried out by thread functions of Java by ensuring the randomness of individual activities.

This paper deals with two-processor scheduling for general acyclic SWITCH-less program nets with random node firing times. First, we introduce a hybrid priority list L* that has been shown to generate optimal schedules for the acyclic SWITCH-less program nets with unity node firing times, of which AND-nodes possess at most single input edge. Then considering the factors of existence of the AND-nodes with two input edges as well as random node firing times, we extend L* to design a new dynamic priority list Ld and four static priority lists {Lsii=1,2,3,4}; and then combining Ld and Lsi (i=1,2,3,4) we propose four hybrid priority lists {L*ii=1,2,3,4}. Finally, we apply genetic algorithm to evaluate the schedules generated by the four lists through simulations on 400 program nets. Our simulation results show two of the four lists can generate reasonably good schedules.

It is proposed a novel method that optimizes nonlinear filters by unsupervised learning using a novel definition of morphological pattern spectrum, called "morphological opening/closing spectrum (MOCS)." The MOCS can separate smaller portions of image objects from approximate shapes even if the shapes are degraded by noisy pixels. Our optimization method analogizes the linear low-pass filtering and Fourier spectrum: filter parameters are adjusted to reduce the portions of smaller sizes in MOCS, since they are regarded as the contributions of noises like high-frequency components. This method has an advantage that it uses only target noisy images and requires no example of ideal outputs. Experimental results of applications of this method to optimization of morphological open-closing filter for binary images are presented.

The genetic algorithm is used to synthesize the directional circular arc array as a sectored antenna. Then, the performance of this sectored antenna in indoor wireless millimeter wave channel is investigated. Based on the desired pattern and the topography of the antennas, the synthesis problem can be reformulated into an optimization problem and solved by the genetic algorithm. The genetic algorithm will always converge to global extreme instead of local extreme and achieves a good approximation to the desired pattern. Next, the impulse responses of the indoor channel for any transmitter-receiver location are computed by shooting and bouncing ray/image techniques. By using the impulse response of multipath channel, the performance of the sectored antenna on BPSK (binary phase shift keying) system with phase and timing recovery circuits is presented. Numerical results show that the synthesized sectored antenna is effective to combat the multipath fading and can increase the transmission rate of indoor millimeter wave system.

In this paper, we present a new approach which is spanning tree-based genetic algorithm for solving a multi-objective transportation problem. The transportation problem as a special type of the network optimization problems has the special data structure in solution characterized as a transportation graph. In encoding transportation problem, we introduce one of node encodings based on a spanning tree which is adopted as it is capable of equally and uniquely representing all possible basic solutions. The crossover and mutation were designed based on this encoding. Also we designed the criterion that chromosome has always feasibility converted to a transportation tree. In the evolutionary process, the mixed strategy with (µ+λ)-selection and roulette wheel selection is used. Numerical experiments show the effectiveness and efficiency of the proposed algorithm.

This paper deals with two-processor scheduling for a class of program nets, that are acyclic and SWITCH-less, and of which each node has unity node firing time. Firstly, we introduce a hybrid priority list L* that generates optimal schedules for the nets whose AND-nodes possess at most single input edge. Then we extend L* to suit for general program nets to give a new priority list L**. Finally, we use genetic algorithm to do the performance evaluation for the schedules generated by L** and show these schedules are quite close to optimal ones.

In this paper, an effective method of system modeling and dynamic scheduling to improve operation and control for the Back-End process of semiconductor manufacturing is developed by using Colored Timed Petri-Nets (CTPNs). The simulator of a CTPNs model was utilized to generate a new heuristic scheduling method with genetic algorithm(GA) which enables us to obtain the optimal values of the weighted delay time and standard deviation of lead time.

This paper shows the design of multi-stage fuzzy inference system with smaller number of rules based upon the optimization of rules by using the genetic algorithm. Since the number of rules of fuzzy inference system increases exponentially in proportion to the number of input variables powered by the number of membership function, it is preferred to divide the inference system into several stages (multi-stage fuzzy inference system) and decrease the number of rules compared to the single stage system. In each stage of inference only a portion of input variables are used as the input, and the output of the stage is treated as an input to the next stage. If we use the simplified inference scheme and assume the shape of membership function is given, the same backpropagation algorithm is available to optimize the weight of each rule as is usually used in the single stage inference system. On the other hand, the shape of the membership function is optimized by using the GA (genetic algorithm) where the characteristics of the membership function is represented as a set of string to which the crossover and mutation operation is applied. By combining the backpropagation algorithm and the GA, we have a comprehensive optimization scheme of learning for the multi-stage fuzzy inference system. The inference system is applied to the automatic bond rating based upon the financial ratios obtained from the financial statement by using the prescribed evaluation of rating published by the rating institution. As a result, we have similar performance of the multi-stage fuzzy inference system as the single stage system with remarkably smaller number of rules.

In this paper, a new feature which is characterized by the extrema density of 2-D wavelet frames estimated at the output of the corresponding filter bank is proposed for texture segmentation. With and without feature selection, the discrimination ability of features based on pyramidal and tree-structured decompositions are comparatively studied using the extrema density, energy, and entropy as features, respectively. These comparisons are demonstrated with separable and non-separable wavelets. With the three-, four-, and five-category textured images from Brodatz album, it is observed that most performances with feature selection improve significantly than those without feature selection. In addition, the experimental results show that the extrema density-based measure performs best among the three types of features investigated. A Min-Min method based on genetic algorithms, which is a novel approach with the spatial separation criterion (SPC) as the evaluation function is presented to evaluate the segmentation performance of each subset of selected features. In this work, the SPC is defined as the Euclidean distance within class divided by the Euclidean distance between classes in the spatial domain. It is shown that with feature selection the tree-structured wavelet decomposition based on non-separable wavelet frames has better performances than the tree-structured wavelet decomposition based on separable wavelet frames and pyramidal decomposition based on separable and non-separable wavelet frames in the experiments. Finally, we compare to the segmentation results evaluated with the templates of the textured images and verify the effectiveness of the proposed criterion. Moreover, it is proved that the discriminatory characteristics of features do spread over all subbands from the feature selection vector.

In this paper, a new scheme for designing multilevel BTC coding is proposed. Optimal quantization can be obtained by selecting the quantization threshold with an exhaustive search. However, this requires an enormous amount of computation and is, thus impractical when we consider an exhaustive search for the multilevel BTC. In order to find a better threshold so that the average mean square error between the original and reconstructed images is a minimum, the genetic algorithm is applied. Comparison of the results of the proposed method with the exhaustive search reveal that the former method can almost achieve optimal quantization with much less computation than that required in the latter case.

An optimun design for N(arbitrary)-sheet capacitive Jaumann elctromagnetic (EM) wave absorber, using genetic algorithm will be presented. This algorithm is a random optimization method based on the genetic relation in the human being. We show the bandwidth for two-sheet capacitive Jaumann absorber can be expanded even more than 108% showed by knott, by using this algorithm and without imposing the double-notch design criteria. We also show that our results approaches knott's results when we restrict the characteristic impedances and lengths of the lines to vary within a very short range. We also design one-sheet and three-sheet capacitive Jaumann absorbers. The only restriction used here is about the meaningful range for the design variables. The goal of this algorithm is that we can impose arbitrary restriction about the range of the variation of the variables. So we can see the performance behaviour with the range dimension of the variables, and we can obtain different optimum results for different ranges. Finally we obtain a 20-dB attenuation bandwidth more than 145% for one-sheet, 173% for two-sheet (compare with 108% obtained in [1]) and 193% for three-sheet capacitive Jaumann EM absorbers, with some acceptable short range for the variables. We design the one-sheet and two-sheet capacitive Jaumann absorbers at low frequency and the three-sheet at high frequency. The 20-dB attenuation bandwidth obtained for the one-sheet and two-sheet capacitive Jaumann absorbers are respectively, from 10 to 77 MHz and, from 4 to 61 MHz. For the three-sheet capacitive Jaumann absorber the 20-dB attenuation bandwidth obtained is, from 0.8 GHz to 280 GHz.

A trinary-phased array, in which a phase quantization unit of phase shifters is 120 degrees is examined. The phase quantization unit of 120 degrees is the roughest value in practical phased array applications. Despite its rough phase quantization, the sidelobe level of less than -9 dB is attained by a genetic algorithm approach.

In this paper, the authors present general views of computer vision and image processing based on optimization. Relaxation and regularization in both broad and narrow senses are used in various fields and problems of computer vision and image processing, and they are currently being combined with general-purpose optimization algorithms. The principle and case examples of relaxation and regularization are discussed; the application of optimization to shape description that is a particularly important problem in the field is described; and the use of a genetic algorithm (GA) as a method of optimization is introduced.

In this letter, a new scheme of designing two-level minimum mean square error quantizer for image coding is proposed. Genetic algorithm is applied to achieve this goal. Comparisons of results with various methods have verified, the proposed method can reach nearly optimal quantization with only less iterations.

We apply some variants of evolutionary computations to the fully-connected neural network model of associative memory. Among others, when we regard it as a parameter optimization problem, we notice that the model has some favorable properties as a test function of evolutionary computations. So far, many functions have been proposed for comparative study. However, as Whitley and his colleagues suggested, many of the existing common test functions have some problems in comparing and evaluating evolutionary computations. In this paper, we focus on the possibilities of using the fully-connected neural network model as a test function of evolutionary computations.

The disk allocation problem examined in this paper is finding a method to distribute a Binary Cartesian Product File on multiple disks to maximize parallel disk I/O accesses for partial match retrieval. This problem is known to be NP-hard, and heuristic approaches have been applied to obtain suboptimal solutions. Recently, efficient methods such as Binary Disk Modulo (BDM) and Error Correcting Code (ECC) methods have been proposed along with the restrictions that the number of disks in which files are stored should be a power of 2. In this paper, a new Disk Allocation method based on Genetic Algorithm (DAGA) is proposed. The DAGA does not place restrictions on the number of disks to be applied and it can allocate the disks adaptively by taking into account the data access patterns. Using the schema theory, it is proven that the DAGA can realize a near-optimal solution with high probability. Comparing the quality of solution derived by the DAGA with the General Disk Modulo (GDM), BDM, and ECC methods through the simulation, shows that 1) the DAGA is superior to the GDM method in all the cases and 2) with the restrictions being placed on the number of disks, the average response time of the DAGA is always less than that of the BDM method and greater than that of the ECC method in the absence of data skew and 3) when data skew is considered, the DAGA performs better than or equal to both BDM and ECC methods, even when restrictions on the number of disks are enforced.

This paper proposes genetic algorithms (GAs) for path planning and trajectory planning of an autonomous mobile robot. Our GA-based approach has an advantage of adaptivity such that the GAs work even if an environment is time-varying or unknown. Therefore, it is suitable for both off-line and on-line motion planning. We first presents a GA for path planning in a 2D terrain. Simulation results on the performance and adaptivity of the GA on randomly generated terrains are shown. Then, we discuss an extension of the GA for solving both path planning and trajectory planning simultaneously.

Retrieving the unknown parameters of scattering objects from measured field data is the subject of microwave imaging. This is naturally and usually posed as an optimization problem. In this paper, micro genetic algorithm coupled with deterministic method is applied to the shape reconstruction of perfectly conducting cylinders. The combined approach, with a very small population like the micro genetic algorithm, performs much better than the conventional large population genetic algorithms (GA's) in reaching the optimal region. In addition, we propose a criterion for switching the micro GA to the deterministic optimizer. The micro GA is utilized to effectively locate the vicinity of the global optimum, while the deterministic optimizer is employed to efficiently reach the optimum after inside this region. Therefore, the combined approach converges to the optimum much faster than the micro GA. The proposed approach is first tested by a function optimization problem, then applied to reconstruct perfectly conducting cylinders from both synthetic data and real data. Impressive and satisfactory results are obtained for both cases, which demonstrate the validity and effectiveness of the proposed approach.

This paper presents a new compound constructive algorithm of neural networks whereby the fuzzy logic technique is explored as an efficient learning algorithm to implement an optimal network construction from an initial simple 3-layer network while the genetic algorithm is used to help design an improved network by evolutions. Numerical simulations on artificial life demonstrate that compared with the existing network design algorithms such as the constructive algorithms, the pruning algorithms and the fixed, static architecture algorithm, the present algorithm, called FuzGa, is efficient in both time complexity and network performance. The improved time complexity comes from the sufficiently small 3 layer design of neural networks and the genetic algorithm adopted partly because the relatively small number of layers facilitates an utilization of an efficient steepest descent method in narrowing down the solution space of fuzzy logic and partly because trappings into local minima can be avoided by genetic algorithm, contributing to considerable saving in time in the processing of network learning and connection. Compared with 54. 8 minutes of MLPs with 65 hidden neurons, 63. 1 minutes of FlexNet or 96. 0 minutes of Pruning, our simulation results on artificial life show that the CPU time of the present method reaching the target fitness value of 100 food elements eaten for the present FuzGa has improved to 42. 3 minutes by SUN's SPARCstation-10 of SuperSPARC 40 MHz machine for example. The role of hidden neurons is elucidated in improving the performance level of the neural networks of the various schemes developed for artificial life applications. The effect of population size on the performance level of the present FuzGa is also elucidated.